Abstract
This study attempts to demonstrate the usefulness of an automated diagnostic procedure based on the Ensemble Empirical Mode Decomposition (EEMD) method and the Support Vector Machine (SVM) for gear fault detection and classification in a two-stage helical gearbox. First, the vibration signals measured on the gearbox casing corresponding to three conditions: normal gear, chipped gear and broken tooth gear are decomposed into different intrinsic modes by EEMD method. The standard SVM is then applied to solve a multi-class problem of gear fault classification. It can be seen from the results obtained at a gearbox test rig that the gear faults can be clearly detected and identified by this approach.
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Dien, N.P., Du, N.T. (2019). On a Diagnostic Procedure to Automatically Classify Gear Faults Using the Vibration Signal Decomposition and Support Vector Machine. In: Fujita, H., Nguyen, D., Vu, N., Banh, T., Puta, H. (eds) Advances in Engineering Research and Application. ICERA 2018. Lecture Notes in Networks and Systems, vol 63. Springer, Cham. https://doi.org/10.1007/978-3-030-04792-4_55
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DOI: https://doi.org/10.1007/978-3-030-04792-4_55
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